论文标题
移动毫米波网络中基于学习的负载平衡移交
Learning-based Load Balancing Handover in Mobile Millimeter Wave Networks
论文作者
论文摘要
毫米波（MMWave）通信是对即将到来的5G和超越通信网络的高数据速率需求的有前途的解决方案。在支持移动方案中的无缝连接时，资源和交换管理是MMWave网络中的两个主要挑战。在本文中，我们共同解决了这两个问题，并在多用户移动MMWave网络中提出了基于学习的负载平衡交换。我们的移交算法选择一个备份基站，并分配资源以最大化所有用户的总和，同时确保目标率阈值并防止过度切换。我们将用户关联建模为非凸优化问题。然后，通过应用深度确定性策略梯度（DDPG）方法，我们近似于优化问题的解决方案。通过模拟，我们表明我们提出的算法最大程度地减少了用户速率低于其最低利率要求的事件数量，并最大程度地减少了移交数量，同时增加了所有用户的总和。
Millimeter-wave (mmWave) communication is a promising solution to the high data rate demands in the upcoming 5G and beyond communication networks. When it comes to supporting seamless connectivity in mobile scenarios, resource and handover management are two of the main challenges in mmWave networks. In this paper, we address these two problems jointly and propose a learning-based load balancing handover in multi-user mobile mmWave networks. Our handover algorithm selects a backup base station and allocates the resource to maximize the sum rate of all the users while ensuring a target rate threshold and preventing excessive handovers. We model the user association as a non-convex optimization problem. Then, by applying a deep deterministic policy gradient (DDPG) method, we approximate the solution of the optimization problem. Through simulations, we show that our proposed algorithm minimizes the number of the events where a user's rate is less than its minimum rate requirement and minimizes the number of handovers while increasing the sum rate of all users.
